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class sklearn.gaussian_process.kernels.PairwiseKernel(gamma=1.0, gamma_bounds=(1e-05, 100000.0), metric='linear', pairwise_kernels_kwargs=None)[source] -
Wrapper for kernels in sklearn.metrics.pairwise.
A thin wrapper around the functionality of the kernels in sklearn.metrics.pairwise.
- Note: Evaluation of eval_gradient is not analytic but numeric and all
- kernels support only isotropic distances. The parameter gamma is considered to be a hyperparameter and may be optimized. The other kernel parameters are set directly at initialization and are kept fixed.
New in version 0.18.
Parameters: gamma: float >= 0, default: 1.0 :
Parameter gamma of the pairwise kernel specified by metric
gamma_bounds : pair of floats >= 0, default: (1e-5, 1e5)
The lower and upper bound on gamma
metric : string, or callable, default: ?linear?
The metric to use when calculating kernel between instances in a feature array. If metric is a string, it must be one of the metrics in pairwise.PAIRWISE_KERNEL_FUNCTIONS. If metric is ?precomputed?, X is assumed to be a kernel matrix. Alternatively, if metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays from X as input and return a value indicating the distance between them.
pairwise_kernels_kwargs : dict, default: None
All entries of this dict (if any) are passed as keyword arguments to the pairwise kernel function.
Methods
clone_with_theta(theta)Returns a clone of self with given hyperparameters theta. diag(X)Returns the diagonal of the kernel k(X, X). get_params([deep])Get parameters of this kernel. is_stationary()Returns whether the kernel is stationary. set_params(\*\*params)Set the parameters of this kernel. -
__init__(gamma=1.0, gamma_bounds=(1e-05, 100000.0), metric='linear', pairwise_kernels_kwargs=None)[source]
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bounds -
Returns the log-transformed bounds on the theta.
Returns: bounds : array, shape (n_dims, 2)
The log-transformed bounds on the kernel?s hyperparameters theta
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clone_with_theta(theta)[source] -
Returns a clone of self with given hyperparameters theta.
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diag(X)[source] -
Returns the diagonal of the kernel k(X, X).
The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated.
Parameters: X : array, shape (n_samples_X, n_features)
Left argument of the returned kernel k(X, Y)
Returns: K_diag : array, shape (n_samples_X,)
Diagonal of kernel k(X, X)
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get_params(deep=True)[source] -
Get parameters of this kernel.
Parameters: deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.
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hyperparameters -
Returns a list of all hyperparameter specifications.
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is_stationary()[source] -
Returns whether the kernel is stationary.
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n_dims -
Returns the number of non-fixed hyperparameters of the kernel.
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set_params(**params)[source] -
Set the parameters of this kernel.
The method works on simple kernels as well as on nested kernels. The latter have parameters of the form
<component>__<parameter>so that it?s possible to update each component of a nested object.Returns: self :
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theta -
Returns the (flattened, log-transformed) non-fixed hyperparameters.
Note that theta are typically the log-transformed values of the kernel?s hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like length-scales naturally live on a log-scale.
Returns: theta : array, shape (n_dims,)
The non-fixed, log-transformed hyperparameters of the kernel
gaussian_process.kernels.PairwiseKernel()
2017-01-15 04:22:38
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